问题描述

我正在处理需要推断不同月份的财务数据.这是我的数据框:

I am dealing with financial data which i need to extrapolate for different months. Here is my dataframe:

invoice_id,date_from,date_to
30492,2019-02-04,2019-09-18

我想将它分解为 date_from 和 date_to 之间的不同月份.因此，我需要为每个月添加行，从开始日期到结束日期.最终输出应如下所示:

I want to break this up for different months between date_from and date_to. Hence i need to add rows for each month with month starting date to ending date. Final output should look like:

invoice_id,date_from,date_to
30492,2019-02-04,2019-02-28
30492,2019-03-01,2019-03-31
30492,2019-04-01,2019-04-30
30492,2019-05-01,2019-05-31
30492,2019-06-01,2019-06-30
30492,2019-07-01,2019-07-31
30492,2019-08-01,2019-08-30
30492,2019-09-01,2019-09-18

还需要处理闰年的情况.pandas datetime 包中是否有任何本机方法可以用来实现所需的输出?

Need to take care of leap year scenario as well. Is there any native method already available in pandas datetime package which i can use to achieve the desired output ?

推荐答案

使用:

print (df)
   invoice_id  date_from    date_to
0       30492 2019-02-04 2019-09-18
1       30493 2019-01-20 2019-03-10

#added months between date_from and date_to
df1 = pd.concat([pd.Series(r.invoice_id,pd.date_range(r.date_from, r.date_to, freq='MS')) 
                 for r in df.itertuples()]).reset_index()
df1.columns = ['date_from','invoice_id']

#added starts of months - sorting for correct positions
df2 = (pd.concat([df[['invoice_id','date_from']], df1], sort=False, ignore_index=True)
         .sort_values(['invoice_id','date_from'])
         .reset_index(drop=True))

#added MonthEnd and date_to  to last rows
mask = df2['invoice_id'].duplicated(keep='last')
s = df2['invoice_id'].map(df.set_index('invoice_id')['date_to'])
df2['date_to'] = np.where(mask, df2['date_from'] + pd.offsets.MonthEnd(), s)

print (df2)
    invoice_id  date_from    date_to
0        30492 2019-02-04 2019-02-28
1        30492 2019-03-01 2019-03-31
2        30492 2019-04-01 2019-04-30
3        30492 2019-05-01 2019-05-31
4        30492 2019-06-01 2019-06-30
5        30492 2019-07-01 2019-07-31
6        30492 2019-08-01 2019-08-31
7        30492 2019-09-01 2019-09-18
8        30493 2019-01-20 2019-01-31
9        30493 2019-02-01 2019-02-28
10       30493 2019-03-01 2019-03-10

这篇关于根据列日期在数据框中添加每个月的行的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持跟版网！